Abstract A nonparametric Bayesian model for histogram clustering is proposed to automatically determine the number of segments when Markov Random Field constraints enforce smooth class assignments. The nonparametric nature of this model is implemented by a Dirichlet process prior to control the number of clusters. The resulting posterior can be sampled by a modification of a conjugate-case sampling algorithm for Dirichlet process mixture models. This sampling procedure estimates segmentations as efficiently as clustering procedures in the strictly conjugate case. The sampling algorithm can process both single-channel and multi-channel image data. Experimental results are presented for real-world synthetic aperture radar and magnetic resonance imaging data.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Smooth Image Segmentation by Nonparametric Bayesian Inference


    Beteiligte:


    Erscheinungsdatum :

    2006-01-01


    Format / Umfang :

    14 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Smooth Image Segmentation by Nonparametric Bayesian Inference

    Orbanz, P. / Buhmann, J. M. | British Library Conference Proceedings | 2006


    Nonparametric Bayesian Image Segmentation

    Orbanz, P. / Buhmann, J. M. | British Library Online Contents | 2008


    Bayesian inference for multiband image segmentation via model-based cluster trees

    Murtagh, F. / Raftery, A. E. / Starck, J. L. | British Library Online Contents | 2005